Application-embedded kernel insights for accelerator selection

Abstract

A method and system of an accelerator selection process is implemented by a computing node, where the computing node has a plurality of accelerators. The method includes receiving a request for an embedded insights-based accelerator selection for an application, determining whether the application includes embedded insights as part of the executable package of the application, and selecting at least one of the plurality of accelerators based on the embedded insights to execute the application.

1 . A method of an accelerator selection process implemented by a computing node, the computing node having a plurality of accelerators, the method comprising:
 receiving a request for an embedded insights-based accelerator selection for an application;   determining whether the application includes embedded insights as part of an executable package of the application; and   selecting at least one of the plurality of accelerators based on the embedded insights to execute the application.   
     
     
         2 . The method of  claim 1 , further comprising:
 determining whether an artificial intelligence model is available for generating profiling insights for the application; and   inferring the profiling insights based on the embedded insights including building insights and preferences insights.   
     
     
         3 . The method of  claim 1 , wherein the selecting the at least one of the plurality of accelerators further comprising:
 estimating insights compliance scores for each of the plurality of accelerators for the application; and   selecting the at least one of the plurality of accelerators based on a best insights compliance score.   
     
     
         4 . The method of  claim 3 , wherein the insights compliance scores are generated from the embedded insights and a capacity specification for each of the plurality of accelerators. 
     
     
         5 . The method of  claim 4 , wherein the insights compliance scores are further generated from profiling insights determined by an artificial intelligence model. 
     
     
         6 . The method of  claim 3 , wherein estimating the insights compliance scores further comprising:
 calculating a first compliance value by comparing a capacity of each of the plurality of accelerators to fulfill building insights in the embedded insights.   
     
     
         7 . The method of  claim 6 , wherein estimating the insights compliance score further comprising:
 calculating a second compliance value by comparing a capacity of each of the plurality of accelerators to fulfill preference insights in the embedded insights.   
     
     
         8 . The method of  claim 7 , wherein estimating the insights compliance score further comprising:
 calculating a third compliance value by comparing a capacity of each of the plurality of accelerators to fulfill profiling insights derived from the embedded insights or an artificial intelligence model.   
     
     
         9 . The method of  claim 8 , wherein estimating the insights compliance score further comprising:
 weighting the first compliance value, the second compliance value, and the third compliance value based on weighting specified by the embedded insights or an accelerator selection algorithm.   
     
     
         10 . The method of  claim 1 , further including training an artificial intelligence model, wherein the training comprises:
 selecting embedded insights for training the artificial intelligence model;   collecting profiling insights for different execution environments and accelerators for the application; and   training the artificial intelligence model using the selected embedded insights and collected profiling insights.   
     
     
         11 . The method of  claim 1 , wherein the application includes serial logic to be executed by at least one general-purpose processor, and data-parallel logic to be executed by the at least one of the plurality of accelerators. 
     
     
         12 . The method of  claim 1 , wherein the embedded insights include any one or more of building insights, profiling insights, and preferences insights. 
     
     
         13 . The method of  claim 12 , wherein the building insights define characteristics of the application collected in a software build process include kernel complexity, kernel identity, or kernel footprint. 
     
     
         14 . The method of  claim 12 , wherein the profiling insights define performance of at least one kernel of the application on at least one execution environment of one of the plurality of accelerators. 
     
     
         15 . The method of  claim 12 , wherein preferences insights define information provided by a developer including any one or more of key performance objective and accelerator affinity. 
     
     
         16 . (canceled) 
     
     
         17 . A computing node comprising:
 a machine readable storage medium having stored therein an application with embedded insights and an accelerator selection process; and   a set of processors including general purpose processors and accelerators to execute the application, wherein the accelerator selection process to:
 receive a request for an embedded insights-based accelerator selection for an application; 
 determine whether the application includes embedded insights as part of an executable package of the application; and 
 select at least one of the accelerators based on the embedded insights to execute the application. 
   
     
     
         18 . A machine-readable storage medium storing computer program code which when executed by a computer carries out functions of an application in an executable package, comprising:
 an executable file including computer program code representing serial logic to be executed by a general purpose processor and data-parallel logic to be executed by an accelerator; and   a set of embedded insights wherein the embedded insights include any one or more of building insights, profiling insights, and preferences insights.   
     
     
         19 . The machine-readable storage medium of  claim 18 , wherein the building insights define characteristics of the application collected in a software build process including kernel complexity, kernel identity, or kernel footprint. 
     
     
         20 . The machine-readable storage medium of  claim 18 , wherein the profiling insights define performance of at least one kernel of the application on at least one execution environment of the accelerator. 
     
     
         21 . The machine-readable storage medium of  claim 18 , wherein preferences insights define information defined by a developer including any one or more of key performance objective and accelerator affinity.